It was revealed that separation of LCP with early LC (n=215) from invasive LCP (n=555) significantly depended on: Hb, leucocytes (abs, total), thrombocytes (abs, tot), erythrocytes (abs, tot), segmented neutrophils (%, abs, total), stick neutrophils (%, abs, total), lymphocytes (%), monocytes (abs, tot), ESS, coagulation time, fibrinogen, cell ratio factors (CRF) (ratio between cancer cells- CC and blood cells subpopulations), tumor size, age, tumor growth, T1-4, G1-3, PT N0---N12 (P=0.046-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed relationships of PT early—invasive cancer and CRF: healthy cells/CC (rank=1), erythrocytes/CC (2), stick neutrophils/CC (3), thrombocytes/CC (4), eosinophils/CC (5), segmented neutrophils/CC (6), lymphocytes/CC (7), monocytes/CC (8), leucocytes/CC (9). Correct classification PT early—invasive cancer was 100% by neural networks computing (area under ROC curve=1.0; error=0.0). CONCLUSION: Blood cell circuit significantly influenced the phase transition early—invasive lung cancer.

1. Phase transition Early-Invasive Lung Cancer and Blood Cell Circuit
No disclosures
OBJECTIVE: The effect of blood cell circuit on the phase transition (PT) early-invasive lung cancer (LC) was
investigated.
METHODS: We analyzed data of 770 consecutive non-small cell LC patients (LCP) (age=57.6±8.3 years; tumor
size=4.1±2.4 cm) radically operated (R0) and monitored in 1985-2021 (m=661, f=109; upper lobectomies=278, lower
lobectomies=177, middle lobectomies=18, bilobectomies=42, pneumonectomies=255, mediastinal lymph node
dissection=770; combined procedures with resection of trachea, carina, atrium, aorta, VCS, vena azygos, pericardium,
liver, diaphragm, ribs, esophagus=194; T1=321, T2=255, T3=133, T4=61; N0=517, N1=131, N2=122, M0=770; G1=194,
G2=243, G3=333; squamous=417, adenocarcinoma=303, large cell=50; early LCP=215; invasive LCP=555; central LC=291;
periferical LC=479. Variables selected for study were input levels of blood cell circuit, sex, age, TNMG. Differences
between groups were evaluated using discriminant analysis, clustering, nonlinear estimation, structural equation
modeling, Monte Carlo, bootstrap simulation and neural networks computing.
RESULTS: It was revealed that separation of LCP with early LC (n=215) from invasive LCP (n=555) significantly
depended on: Hb, leucocytes (abs, total), thrombocytes (abs, tot), erythrocytes (abs, tot), segmented neutrophils (%, abs,
total), stick neutrophils (%, abs, total), lymphocytes (%), monocytes (abs, tot), ESS, coagulation time, fibrinogen, cell ratio
factors (CRF) (ratio between cancer cells- CC and blood cells subpopulations), tumor size, age, tumor growth, T1-4, G1-3,
PT N0---N12 (P=0.046-0.000). Neural networks computing, genetic algorithm selection and bootstrap simulation revealed
relationships of PT early—invasive cancer and CRF: healthy cells/CC (rank=1), erythrocytes/CC (2), stick neutrophils/CC
(3), thrombocytes/CC (4), eosinophils/CC (5), segmented neutrophils/CC (6), lymphocytes/CC (7), monocytes/CC (8),
leucocytes/CC (9). Correct classification PT early—invasive cancer was 100% by neural networks computing (area under
ROC curve=1.0; error=0.0).
CONCLUSION: Blood cell circuit significantly influenced the phase transition early—invasive lung cancer.
Oleg Kshivets, MD, PhD e-mail: okshivets@yahoo.com
Surgery Department, Roshal Hospital, Moscow, Russia a
Effect
GLZ results Distribution :
NORMAL Link function: LOG
Degr. of
Freedom
Wald
Stat.
p
Intercept 1 7.0733 0.007824
Tumor Size 1 17.0855 0.000036
Erythrocytes 1 26.7452 0.000000
Eosinophils 1 10.1318 0.001457
Stick Neutrophils 1 5.3924 0.020225
Segmented Neutrophils 1 7.1446 0.007519
Lymphocytes 1 6.7744 0.009248
Monocytes 1 6.6979 0.009653
Monocytes (abs) 1 6.4758 0.010935
Leucocytes/Cancer Cells 1 12.4650 0.000415
Eosinophils/Cancer Cells 1 5.5420 0.018566
Segmented Neutrophils/Cancer Cells 1 9.1786 0.002449
Lymphocytes/Cancer Cells 1 7.9876 0.004710
Healthy Cells/Cancer Cells 1 54.3179 0.000000
Erythrocytes (tot) 1 25.8367 0.000000
Leucocytes (tot) 1 12.3061 0.000451
Eosinophils (tot) 1 16.5695 0.000047
Stick Neutrophils (tot) 1 9.7226 0.001820
Stick Neutrophils (tot) 1 12.3151 0.000449
Lymphocytes (tot) 1 11.6562 0.000640
Monocytes (tot) 1 5.3094 0.021211
Tumor Growth 1 10.0305 0.001540
G1-3 2 14.4966 0.000711
T1-4 3 145.8011 0.000000
Phase Transition N0----N12 1 61.7560 0.000000
Neural Networks: n=770;
Baseline Error=0.000;
Area under ROC Curve=1.000;
Correct Classification Rate=100%
Rank Sensitivity
Healthy Cells/Cancer Cells 1 2639
Erythrocytes/Cancer Cells 2 900
Stick Neutrophils/Cancer Cells 3 821
Thrombocytes/Cancer Cells 4 709
Eosinophils/Cancer Cells 5 606
Segmented Neutrophils/Cancer Cells 6 485
Lymphocytes/Cancer Cells 7 372
Monocytes/Cancer Cells 8 342
Leucocytes/Cancer Cells Stick 9 216
Bootstrap Simulation n=770
Significant Factors
(Number of Samples=3333)
Rank Kendal Tau-A P<
Healthy Cells/Cancer Cells 1 0.607 0.000
Erythrocytes/Cancer Cells 2 0.596 0.000
Thrombocytes/Cancer Cells 3 0.561 0.000
Leucocytes/Cancer Cells 4 0.498 0.000
Segmented Neutrophils/Cancer Cells 5 0.472 0.000
Lymphocytes/Cancer Cells 6 0.450 0.000
Tumor Size 7 -0.399 0.000
T1-4 8 -0.317 0.000
Monocytes/Cancer Cells 9 0.263 0.000
Eosinophils/Cancer Cells 10 0.233 0.000
Phase Transition N0---N12 11 -0.183 0.000
Surgery along 12 0.151 0.000
Pneumonectomies/Lobectomies 13 -0.149 0.000
ESS 14 -0.149 0.000
Procedure Type 15 0.148 0.000
Segmented Neutrophils (abs) 16 -0.125 0.000
Coagulation Time 17 -0.109 0.000
G1-3 18 -0.091 0.001
Adjuvant Treatment 19 -0.089 0.001
Tumor Growth 20 0.079 0.05
Stick Neutrophils (abs) 21 -0.076 0.05
Stick Neutrophils (%) 22 -0.074 0.05
Histology 23 0.073 0.05
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